Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Masoud Ravansalar and Taher Rajaee and Ozgur Kisi",
  title =        "Wavelet-linear genetic programming: A new approach for
                 modeling monthly streamflow",
  journal =      "Journal of Hydrology",
  volume =       "549",
  pages =        "461--475",
  year =         "2017",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2017.04.018",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022169417302378",
  abstract =     "The streamflows are important and effective factors in
                 stream ecosystems and its accurate prediction is an
                 essential and important issue in water resources and
                 environmental engineering systems. A hybrid
                 wavelet-linear genetic programming (WLGP) model, which
                 includes a discrete wavelet transform (DWT) and a
                 linear genetic programming (LGP) to predict the monthly
                 streamflow (Q) in two gauging stations, Pataveh and
                 Shahmokhtar, on the Beshar River at the Yasuj, Iran
                 were used in this study. In the proposed WLGP model,
                 the wavelet analysis was linked to the LGP model where
                 the original time series of streamflow were decomposed
                 into the sub-time series comprising wavelet
                 coefficients. The results were compared with the single
                 LGP, artificial neural network (ANN), a hybrid
                 wavelet-ANN (WANN) and Multi Linear Regression (MLR)
                 models. The comparisons were done by some of the
                 commonly used relevant physical statistics. The Nash
                 coefficients (E) were found as 0.877 and 0.817 for the
                 WLGP model, for the Pataveh and Shahmokhtar stations,
                 respectively. The comparison of the results showed that
                 the WLGP model could significantly increase the
                 streamflow prediction accuracy in both stations. Since,
                 the results demonstrate a closer approximation of the
                 peak streamflow values by the WLGP model, this model
                 could be used for the simulation of cumulative
                 streamflow data prediction in one month ahead.",
  keywords =     "genetic algorithms, genetic programming, Streamflow,
                 Discrete wavelet transform, Artificial neural network,
                 Beshar River",

Genetic Programming entries for Masoud Ravansalar Taher Rajaee Ozgur Kisi